pyspark.pandas.Series.std¶
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Series.std(axis: Union[int, str, None] = None, skipna: bool = True, ddof: int = 1, numeric_only: bool = None) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, Series]¶ Return sample standard deviation.
New in version 3.3.0.
- Parameters
- axis: {index (0), columns (1)}
Axis for the function to be applied on.
- skipna: bool, default True
Exclude NA/null values when computing the result.
Changed in version 3.4.0: Supported including NA/null values.
- ddof: int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
Changed in version 3.4.0: Supported including arbitary integers.
- numeric_only: bool, default None
Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.
- Returns
- std: scalar for a Series, and a Series for a DataFrame.
Examples
>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]}, ... columns=['a', 'b'])
On a DataFrame:
>>> df.std() a 1.0 b 0.1 dtype: float64
>>> df.std(ddof=2) a 1.414214 b 0.141421 dtype: float64
>>> df.std(axis=1) 0 0.636396 1 1.272792 2 1.909188 3 NaN dtype: float64
>>> df.std(ddof=0) a 0.816497 b 0.081650 dtype: float64
On a Series:
>>> df['a'].std() 1.0
>>> df['a'].std(ddof=0) 0.816496580927726
>>> df['a'].std(ddof=-1) 0.707106...